jenshnielsen/problem_fits_file.ipynb

## problem_fits_file.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jhn/Envs/mpl2x/lib/python3.5/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/Users/jhn/Envs/mpl2x/lib/python3.5/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pylab import *\n",
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Just making a 10x10 meshgrid\n",
    "\n",
    "x = np.arange(10)\n",
    "X , Y = np.meshgrid(x,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# finding the distance of different points on the meshgrid from a point suppose at (5,5)\n",
    "\n",
    "Z = ((X-5)**2 + (Y-5)**2)**0.5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104f51198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "imshow(Z, origin = \"lower\")\n",
    "colorbar()\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# writing the Z data into a fits file\n",
    "\n",
    "fits.writeto(\"my_file.fits\", Z, clobber=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# reading the same fits file and storing the data\n",
    "\n",
    "Z_read = fits.open(\"my_file.fits\")[0].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h+wzbD9o+bPtx23vablMVtmdsP2r7m223ZRTbp9neZ/vJ4Z/xZW23aRTbnxl+Dx6zfbft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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106ef3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting Z_read : we expect it to show the same plot as before\n",
    "\n",
    "%matplotlib inline\n",
    "imshow(Z_read, origin = \"lower\")\n",
    "colorbar()\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "# Lo! That's not the case! It's not the same plot!\n",
    "\n",
    "# Hence, I try to check whether the values stored in Z and Z_read are different?\n",
    "\n",
    "print(Z - Z_read)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# No! They are same! I don't get why the plots look different!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.07106781,  6.40312424,  5.83095189,  5.38516481,  5.09901951,\n",
       "         5.        ,  5.09901951,  5.38516481,  5.83095189,  6.40312424],\n",
       "       [ 6.40312424,  5.65685425,  5.        ,  4.47213595,  4.12310563,\n",
       "         4.        ,  4.12310563,  4.47213595,  5.        ,  5.65685425],\n",
       "       [ 5.83095189,  5.        ,  4.24264069,  3.60555128,  3.16227766,\n",
       "         3.        ,  3.16227766,  3.60555128,  4.24264069,  5.        ],\n",
       "       [ 5.38516481,  4.47213595,  3.60555128,  2.82842712,  2.23606798,\n",
       "         2.        ,  2.23606798,  2.82842712,  3.60555128,  4.47213595],\n",
       "       [ 5.09901951,  4.12310563,  3.16227766,  2.23606798,  1.41421356,\n",
       "         1.        ,  1.41421356,  2.23606798,  3.16227766,  4.12310563],\n",
       "       [ 5.        ,  4.        ,  3.        ,  2.        ,  1.        ,\n",
       "         0.        ,  1.        ,  2.        ,  3.        ,  4.        ],\n",
       "       [ 5.09901951,  4.12310563,  3.16227766,  2.23606798,  1.41421356,\n",
       "         1.        ,  1.41421356,  2.23606798,  3.16227766,  4.12310563],\n",
       "       [ 5.38516481,  4.47213595,  3.60555128,  2.82842712,  2.23606798,\n",
       "         2.        ,  2.23606798,  2.82842712,  3.60555128,  4.47213595],\n",
       "       [ 5.83095189,  5.        ,  4.24264069,  3.60555128,  3.16227766,\n",
       "         3.        ,  3.16227766,  3.60555128,  4.24264069,  5.        ],\n",
       "       [ 6.40312424,  5.65685425,  5.        ,  4.47213595,  4.12310563,\n",
       "         4.        ,  4.12310563,  4.47213595,  5.        ,  5.65685425]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('>f8')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_read.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Matplotlib 2.x Python 3",
   "language": "python",
   "name": "matplotlib2x"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/Users/jhn/Envs/mpl2x/lib/python3.5/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
	" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
	"/Users/jhn/Envs/mpl2x/lib/python3.5/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
	" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
	]
	}
	],
	"source": [
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"import numpy as np\n",
	"from pylab import *\n",
	"from astropy.io import fits"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# Just making a 10x10 meshgrid\n",
	"\n",
	"x = np.arange(10)\n",
	"X , Y = np.meshgrid(x,x)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# finding the distance of different points on the meshgrid from a point suppose at (5,5)\n",
	"\n",
	"Z = ((X-5)2 + (Y-5)2)**0.5 "
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"image/png": 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DoDmRNMGPxXi7pCeTPJVkRdK3JH2k3moBaMSktngkXSbp6XWvD0p6R/GaAGjYZG+Z8Gt8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\nGAA1AAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x104f51198>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"%matplotlib inline\n",
	"imshow(Z, origin = \"lower\")\n",
	"colorbar()\n",
	"show()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# writing the Z data into a fits file\n",
	"\n",
	"fits.writeto(\"my_file.fits\", Z, clobber=True)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# reading the same fits file and storing the data\n",
	"\n",
	"Z_read = fits.open(\"my_file.fits\")[0].data"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"image/png": 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h+wzbD9o+bPtx23vablMVtmdsP2r7m223ZRTbp9neZ/vJ4Z/xZW23aRTbnxl+Dx6zfbft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\nRU5ErkJggg==\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x106ef3dd8>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"# plotting Z_read : we expect it to show the same plot as before\n",
	"\n",
	"%matplotlib inline\n",
	"imshow(Z_read, origin = \"lower\")\n",
	"colorbar()\n",
	"show()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
	" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
	]
	}
	],
	"source": [
	"# Lo! That's not the case! It's not the same plot!\n",
	"\n",
	"# Hence, I try to check whether the values stored in Z and Z_read are different?\n",
	"\n",
	"print(Z - Z_read)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# No! They are same! I don't get why the plots look different!"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"array([[ 7.07106781, 6.40312424, 5.83095189, 5.38516481, 5.09901951,\n",
	" 5. , 5.09901951, 5.38516481, 5.83095189, 6.40312424],\n",
	" [ 6.40312424, 5.65685425, 5. , 4.47213595, 4.12310563,\n",
	" 4. , 4.12310563, 4.47213595, 5. , 5.65685425],\n",
	" [ 5.83095189, 5. , 4.24264069, 3.60555128, 3.16227766,\n",
	" 3. , 3.16227766, 3.60555128, 4.24264069, 5. ],\n",
	" [ 5.38516481, 4.47213595, 3.60555128, 2.82842712, 2.23606798,\n",
	" 2. , 2.23606798, 2.82842712, 3.60555128, 4.47213595],\n",
	" [ 5.09901951, 4.12310563, 3.16227766, 2.23606798, 1.41421356,\n",
	" 1. , 1.41421356, 2.23606798, 3.16227766, 4.12310563],\n",
	" [ 5. , 4. , 3. , 2. , 1. ,\n",
	" 0. , 1. , 2. , 3. , 4. ],\n",
	" [ 5.09901951, 4.12310563, 3.16227766, 2.23606798, 1.41421356,\n",
	" 1. , 1.41421356, 2.23606798, 3.16227766, 4.12310563],\n",
	" [ 5.38516481, 4.47213595, 3.60555128, 2.82842712, 2.23606798,\n",
	" 2. , 2.23606798, 2.82842712, 3.60555128, 4.47213595],\n",
	" [ 5.83095189, 5. , 4.24264069, 3.60555128, 3.16227766,\n",
	" 3. , 3.16227766, 3.60555128, 4.24264069, 5. ],\n",
	" [ 6.40312424, 5.65685425, 5. , 4.47213595, 4.12310563,\n",
	" 4. , 4.12310563, 4.47213595, 5. , 5.65685425]])"
	]
	},
	"execution_count": 11,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"Z_read"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"dtype('float64')"
	]
	},
	"execution_count": 12,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"Z.dtype"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 13,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"dtype('>f8')"
	]
	},
	"execution_count": 13,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"Z_read.dtype"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Matplotlib 2.x Python 3",
	"language": "python",
	"name": "matplotlib2x"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.5.2"
	},
	"widgets": {
	"state": {},
	"version": "1.1.2"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}